Filtering approach based on empirical mode decomposition improves the assessment of short scale complexity in long QT syndrome type 1 population

Annu Int Conf IEEE Eng Med Biol Soc. 2014:2014:6671-4. doi: 10.1109/EMBC.2014.6945158.


This study assesses the complexity of heart period (HP) and QT variability series through sample entropy (SampEn) in long QT syndrome type 1 individuals. In order to improve signal-to-noise ratio SampEn was evaluated over the original series (SampEn0) and over the residual computed by subtracting the first oscillatory mode identified by empirical mode decomposition (SampEn(EMD1R)). HP and QT interval were continuously extracted during daytime (2:00-6:00 PM) from 24 hour Holter recordings in 14 non mutation carriers (NMCs) and 34 mutation carriers (MCs) subdivided in 11 asymptomatic (ASYMP) and 23 symptomatic (SYMP). Both NMCs and MCs belonged to the same family line. While SampEn0 did not show differences among the three groups, Samp(EnEMD1R) assessed over the QT series significantly decreased in ASYMP subjects. SampEn(EMD1R) identified a possible factor (i.e. the lower short scale QT complexity) that might contribute to the different risk profile of the ASYMP group.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Electrocardiography
  • Electrocardiography, Ambulatory
  • Entropy
  • Female
  • Heart Rate
  • Humans
  • Male
  • Middle Aged
  • Romano-Ward Syndrome / diagnosis*
  • Romano-Ward Syndrome / physiopathology
  • Signal Processing, Computer-Assisted*
  • Signal-To-Noise Ratio
  • Young Adult